SOFTWARE ENGINEERING PRACTICES IN DEVELOPING DEEP LEARNING MODELS: AN INDUSTRIAL CASE VALIDATION

Abstract: The widespread of machine learning and deep learning in commercial and industrial settings has seen a dramatic up-rise. While the traditional software engineering techniques have overlap between machine learning model development, fundamental differences exist which affect both scientific disciplines. The current state-of-the-art argues that most challenges in software engineering of deep learning applications stem from poorly defined software requirements, tightly coupled architecturesand hardware-induced development issues. However the majority of the current work on this topic stems from literature reviews and requires validation in an industrial context. The work aims to validate the findings of the academia through the development of the autoencoder model for gearbox fault detection. The model has been developed as a part of the ongoing campaign from Volvo Construction Equipment towards introducing AI-based solution in quality control and production. Findings of the work are mostly aligned with the current state-of-the-art, where poorly defined software requirements and hardware-induced issues have been experienced, but the tightly-coupled architecture did not characterize the final product. Along with the confirmation of the previous findings, the work presents a recommendation for practitioners of software engineering for deep learning models in the form of technological rule which addresses the hardware-induced issues of development through the contribution of a method for calculating the memory requirements of the model and batch during the training phase.